Exploiting Surroundedness for Saliency Detection: A Boolean Map Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Jianming Zhang, Stan Sclaroff

Abstract

We demonstrate the usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS). In our formulation, an image is characterized by a set of binary images, which are generated by randomly thresholding the image's feature maps in a whitened feature space. Based on a Gestalt principle of figure-ground segregation, BMS computes a saliency map by discovering surrounded regions via topological analysis of Boolean maps. Furthermore, we draw a connection between BMS and the Minimum Barrier Distance to provide insight into why and how BMS can properly captures the surroundedness cue via Boolean maps. The strength of BMS is verified by its simplicity, efficiency and superior performance compared with 10 state-of-the-art methods on seven eye tracking benchmark datasets.

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Citations

Dec 15, 2015·Proceedings of the National Academy of Sciences of the United States of America·Matthias KümmererMatthias Bethge
Feb 11, 2015·Vision Research·Neil D B BruceJohn K Tsotsos
Aug 3, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Ke GuDaniel Thalmann
Jan 11, 2019·Sensors·Pawel KasprowskiPrzemysław Skurowski
Apr 30, 2019·Royal Society Open Science·Albert End, Matthias Gamer
Apr 13, 2018·Frontiers in Psychology·Mateja Marić, Dražen Domijan
May 6, 2019·Optics Express·Zhe ChenYunbo Xiong
Oct 20, 2020·Frontiers in Computational Neuroscience·Takeshi UejimaRalph Etienne-Cummings

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